A recurrent neuro-fuzzy system and its application in inferential sensing

Conventional neuro-fuzzy systems cannot effectively cope with dynamic processes, such as the heating systems of the buildings, due to the feed forward network structure. To overcome this problem, the existing hybrid system is incorporated with a feedback loop so that it can model the dynamical behavior of the process. As a case study, this improved hybrid system is employed to build an inferential model that estimates the average air temperature in a building served by a forced-warm-air heating system. The results show that the inferential model based on this improved hybrid system is accurate and robust. The parameter identification and tuning process is effective. Compared with conventional hybrid neuro-fuzzy system, it can significantly improve the performance of the inferential models. The neuro-fuzzy model incorporated with the feed-back loop has been tested using experimental data and the worst monthly RMSE as 0.062 °C. This means that the inferential model can be employed to design better control schemes to improve indoor environmental quality and to save energy in buildings.